initial model commit

README.md

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+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: en de nl es
+datasets:
+- conll2003
+inference: false
+---
+
+## 4-Language NER in Flair (English, German, Dutch and Spanish)
+
+This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
+
+F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised), **88,96** (CoNLL-03 Dutch), **86,65** (CoNLL-03 Spanish)
+
+
+Predicts 4 tags:
+
+| **tag**                        | **meaning** |
+|---------------------------------|-----------|
+| PER         | person name | 
+| LOC         | location name | 
+| ORG         | organization name | 
+| MISC         | other name | 
+
+Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
+
+---
+
+### Demo: How to use in Flair
+
+Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
+
+```python
+from flair.data import Sentence
+from flair.models import SequenceTagger
+
+# load tagger
+tagger = SequenceTagger.load("flair/ner-multi")
+
+# make example sentence in any of the four languages
+sentence = Sentence("George Washington ging nach Washington")
+
+# predict NER tags
+tagger.predict(sentence)
+
+# print sentence
+print(sentence)
+
+# print predicted NER spans
+print('The following NER tags are found:')
+# iterate over entities and print
+for entity in sentence.get_spans('ner'):
+    print(entity)
+
+```
+
+This yields the following output:
+```
+Span [1,2]: "George Washington"   [− Labels: PER (0.9977)]
+Span [5]: "Washington"   [− Labels: LOC (0.9895)]
+```
+
+So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*". 
+
+
+---
+
+### Training: Script to train this model
+
+The following Flair script was used to train this model: 
+
+```python
+from flair.data import Corpus
+from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH
+from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
+
+# 1. get the multi-language corpus
+corpus: Corpus = MultiCorpus([
+    CONLL_03(),         # English corpus
+    CONLL_03_GERMAN(),  # German corpus
+    CONLL_03_DUTCH(),   # Dutch corpus
+    CONLL_03_SPANISH(), # Spanish corpus
+    ])
+
+# 2. what tag do we want to predict?
+tag_type = 'ner'
+
+# 3. make the tag dictionary from the corpus
+tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
+
+# 4. initialize each embedding we use
+embedding_types = [
+
+    # GloVe embeddings
+    WordEmbeddings('glove'),
+
+    # FastText embeddings
+    WordEmbeddings('de'),
+
+    # contextual string embeddings, forward
+    FlairEmbeddings('multi-forward'),
+
+    # contextual string embeddings, backward
+    FlairEmbeddings('multi-backward'),
+]
+
+# embedding stack consists of Flair and GloVe embeddings
+embeddings = StackedEmbeddings(embeddings=embedding_types)
+
+# 5. initialize sequence tagger
+from flair.models import SequenceTagger
+
+tagger = SequenceTagger(hidden_size=256,
+                        embeddings=embeddings,
+                        tag_dictionary=tag_dictionary,
+                        tag_type=tag_type)
+
+# 6. initialize trainer
+from flair.trainers import ModelTrainer
+
+trainer = ModelTrainer(tagger, corpus)
+
+# 7. run training
+trainer.train('resources/taggers/ner-multi',
+              train_with_dev=True,
+              max_epochs=150)
+```
+
+
+
+---
+
+### Cite
+
+Please cite the following paper when using this model.
+
+```
+@inproceedings{akbik2018coling,
+  title={Contextual String Embeddings for Sequence Labeling},
+  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
+  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
+  pages     = {1638--1649},
+  year      = {2018}
+}
+```